Certified GenAI in Life Sciences (CGAI-LS) Certification Program by Tonex
Generative AI is accelerating discovery across biology, medicine, and pharma. This program equips professionals to apply GenAI responsibly from target identification to clinical decision support. You will learn how large models generate molecules, optimize proteins and vaccines, and integrate multimodal evidence from text, imaging, and genomics. Emphasis is placed on validation, interpretability, and regulatory readiness for GxP and clinical contexts.
We also cover governance, auditability, and risk controls so models meet safety and quality expectations. Cybersecurity matters here. Healthcare and biotech data are high-value targets; you will learn threat-aware design, secure data pipelines, and protections for model assets. The result is a practical, cross-functional toolkit to move GenAI from pilots to production in regulated environments with confidence.
Learning Objectives:
- Explain GenAI foundations for biology, medicine, and pharma.
- Generate and evaluate candidates for small molecules and biologics.
- Design therapeutic proteins and vaccine constructs with AI guidance.
- Build multimodal pipelines linking text, images, and omics.
- Apply GxP/HIPAA/IVDR-aligned validation and documentation.
- Implement security, privacy, and safety controls for GenAI systems.
Audience:
- R&D and Clinical Data Scientists
- Bioinformaticians and Computational Biologists
- Pharma and Biotech Product Managers
- Healthcare IT and Data Engineers
- Regulatory and Quality Professionals
- Cybersecurity Professionals
Program Modules:
Module 1: GenAI Foundations for Life Sciences
- Generative model types and mechanisms
- Foundation models vs domain-specific adapters
- Data readiness for biomedical corpora
- Prompting, RAG, and constrained generation
- Trust, bias, and explainability basics
- KPIs: accuracy, novelty, and utility
Module 2: Molecular & Compound Generation
- SMILES/graph-based molecule generation
- Property prediction and ADMET prioritization
- Synthesizability and retrosynthesis checks
- Virtual screening at scale
- Active learning and feedback loops
- Hit expansion and triage workflows
Module 3: Therapeutic Protein & Vaccine Design
- Sequence and structure co-design
- Epitope mapping and antigen selection
- Stability, immunogenicity, and developability
- Docking and binder optimization
- In-silico variants and fitness scoring
- Documentation for preclinical packages
Module 4: Personalized Medicine & Precision Oncology
- Patient stratification with multi-omics
- Digital pathology and radiology integration
- Biomarker discovery and companion diagnostics
- Treatment recommendation support
- Real-world evidence curation
- Safety monitoring and drift detection
Module 5: Multimodal AI Pipelines
- Text–image–genomics fusion strategies
- Knowledge graphs and ontology alignment
- RAG with scientific literature and EHRs
- Workflow orchestration and data lineage
- Human-in-the-loop review gates
- Performance monitoring and alerts
Module 6: Governance, Compliance & Security
- GxP documentation and audit trails
- HIPAA/IVDR/21 CFR Part 11 alignment
- Model risk assessment and guardrails
- Data privacy, PHI protection, DPIA
- Secure MLOps and supply chain controls
- Incident response and post-market surveillance
Exam Domains:
- GenAI Safety and Biosecurity Risk Management
- Data Governance and Regulatory Compliance
- Clinical Validation and Evidence Generation
- Model Lifecycle and Operational Excellence
- Ethics, Privacy, and Patient Rights
- Enterprise Deployment and Change Leadership
Course Delivery:
The course is delivered through expert-led lectures, interactive discussions, case studies, and project-based learning tailored to Certified GenAI in Life Sciences (CGAI-LS). Participants access curated online resources, readings, and guided exercises designed for regulated settings.
Assessment and Certification:
Participants are assessed through quizzes, assignments, and a capstone project. Upon successful completion, participants receive a certificate in Certified GenAI in Life Sciences (CGAI-LS).
Question Types:
- Multiple Choice Questions (MCQs)
- Scenario-based Questions
Passing Criteria:
To pass the Certified GenAI in Life Sciences (CGAI-LS) Certification Training exam, candidates must achieve a score of 70% or higher.
Ready to lead GenAI in healthcare and biotech? Enroll now. Build compliant, secure, and effective solutions that deliver impact from discovery to clinic.